基于深层图卷积的EEG情绪识别方法研究  

Research on EEG emotion recognition method based on deep graph convolutional

作　　者：李奇[1,2] 常立娜 武岩[1,2] 闫旭荣 Li Qi;Chang Lina;Wu Yan;Yan Xurong(School of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,China;Zhongshan Institute of Changchun University of Science and Technology,Zhongshan 528400,China)

机构地区：[1]长春理工大学计算机科学技术学院,长春130022 [2]长春理工大学中山研究院,中山528400

出　　处：《电子测量技术》2024年第4期18-22,共5页Electronic Measurement Technology

基　　金：吉林省科技发展计划国际科技合作项目(20200801035GH);吉林省科技发展计划国际联合研究中心建设项目(20200802004GH)资助。

摘　　要：针对浅层图卷积提取的局部脑区空间关联信息对情感脑电表征不足的问题,本文提出了一种深层图卷积网络模型。该模型利用深层图卷积学习情绪脑电全局通道间的内在关系,在卷积传播过程中应用残差连接和权重自映射解决深层图卷积网络面临的节点特征收敛到固定空间无法学习到有效特征的问题,并在卷积层后加入PN正则化扩大不同情绪特征间的距离,提高情绪识别的性能。在SEED数据集上进行实验,与浅层图卷积网络相比准确率提高了0.7%,标准差下降了3.15。结果表明该模型提取的全局脑区空间关联信息对情绪识别的有效性。To address the issue of insufficient spatial correlation information for emotion EEG characterization extracted by shallow graph convolution,this paper proposes a deep graph convolutional network model.The model utilizes deep graph convolution to learn the intrinsic relationships among global channels of emotional EEG,applying residual connections and weight self-mapping during the convolutional propagation process to address the problem of node features in deep graph convolution networks converging to a fixed space and failing to learn effective features.Additionally,PN regularization is added after the convolutional layer to expand the distance between different emotional features and improve emotion recognition performance.Experimental results on the SEED dataset show that compared to shallow graph convolution networks,the accuracy of the proposed model has increased by 0.7%while the standard deviation has decreased by 3.15.These results demonstrate the effectiveness of the global brain region spatial correlation information extracted by this model for emotion recognition.

关 键 词：脑电信号 情绪识别 深度图卷积神经网络 全局脑区 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]